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Data Literacy

This is a collection of information resources on data literacy. Users can find links to courses, workshops, tools, tutorials, videos, webinars, and other resources related to data literacy.

Overview of NIH DMSP

NIH has issued the new Data Management and Sharing (DMS) Policy (effective for due dates on/after January 25, 2023) to promote the sharing of scientific data that are generated from NIH-funded or conducted research.

Scientific Data is defined as data commonly accepted in the scientific community as of sufficient quality to validate and replicate research findings, regardless of whether the data are used to support scholarly publications, including any data needed to validate and replicate research findings. It does not include laboratory notebooks, preliminary analyses, completed case report forms, drafts of scientific papers, plans for future research, peer reviews, communications with colleagues, or physical objects such as laboratory specimens.

Applicants planning to generate scientific data will submit DMS Plans to NIH as part of the funding application or proposal. They can submit DMS plans and budget requests as part of the funding application or proposal. And the awardees must comply with the approved DMS plans as a term and condition of award.

Researchers may download the simplified version of the Data Management and Sharing Policy Overview Page (PDF) as a quick guide.


Elements of an NIH Data Management and Sharing Plan

1.Data Type:

Summarize the types and amount of scientific data to be generated and/or used in the research. NIH does not anticipate that researchers will preserve  and share all scientific data generated in a study. Describe which scientific data will be preserved and shared based on ethical, legal, and technical factors and the rationale for the decisions. List the metadata, other relevant data, and associated documentation to be used to facilitate interpretation of the scientific data.

2. Related Tools, Software and/or Code:

Provide the name(s) of the needed tool(s) and software and specify how they can be accessed (e.g., open source and freely available, or fee based, or to obtain them from the research team). Will the tools remain available for as long as the scientific data remain available?

3. Standards:

Data standards refer to methods of organizing, documenting, and formatting data in order to aid in data aggregation, sharing and reuse. Describe what standards will be applied to the data and associated metadata. (PIDs, metadata standard, encoding standard, common data standard, etc.)

4. Data Preservation, Access, and Associated Timelines: 

This element is about plans and timelines for data preservation and access, including:

1) Provide the name of the repository(ies) where the data will be archived. Select a Data Repository, if there is no specified repository, to preserve data and provide long term access.

2) Indicate how the scientific data will be findable and identifiable. Will it be via a persistent unique identifier or other standard indexing tools? 

3) Describe when the scientific data will be made available to other users (i.e., no later than time of an associated publication or end of the performance period, whichever comes first) and for how long data will be made available.

5. Access, Distribution, or Reuse Considerations


1) any applicable factors that affect subsequent access, distribution, or reuse of data related to Informed consent, Privacy and confidentiality protections, etc.

2) Whether access to scientific data will be controlled.

3) If generating scientific data derived from humans, describe how the privacy, rights, and confidentiality of human research participants will be protected (e.g., through de-identification, Certificates of Confidentiality, and other protective measures).

6. Oversight of Data Management and Sharing:

This element refers to oversight by the funded institution, rather than by NIH. The DMS Policy does not create any expectations about who will be responsible for Plan oversight at the institution. Indicate how compliance with the DMS Plan will be monitored and managed, the frequency of oversight, and by whom (e.g., title, roles).  


How to Identity a Data Repository

When in selecting a data repository, NIH recommends researchers to select a repository that is most appropriate for their data type and discipline to maximize discovery and reuse. NIH also strongly encourages the use of established quality repositories for preserving and sharing scientific data to achieving the FAIRness of the data. FAIR refers to finable, accessible, interoperability, and reusable.

Repositories for Sharing Scientific Data | Data Sharing ( 

Open Domain-Specific Data Sharing Repositories (

• Generalist Repositories - NIH encourages researchers to use domain-specific repositories. If researchers do not have a domain-specific repository available for their data, then they may consider using a generalist to share data. Here is a comparison of the generalists at Generalist Repository Comparison Chart. 
 "When investigators cannot locate a repository for their discipline or the type of data they generate, a generalist repository can be a useful place to share data. Generalist repositories accept data regardless of data type, format, content, or disciplinary focus. NIH does not recommend a specific generalist repository and the list below, which is not exhaustive, is provided as a guide for locating generalist repositories." - source:

• The Registry of Research Data Repositories (

• Researchers may also consider a local institutional repository. SOAR accepts datasets of up to 5 GB per file. Data deposited in SOAR are assigned a permanent DOI, made available through the SOAR website, and indexed by Google and other search engines where they can be discovered by researchers worldwide. 


DMPTool & Sample Plans

Researchers can use the DMPTool to develop their DMS plans. Rutgers is an institution member of DMPTool. Rutgers users may log in using their Rutgers NetID.

- Free and accessible at

- Developed by the University of California Curation Center of the California Digital Library

- Rutgers is an institutional member.

- Supports major funders (e.g. NIH, NSF, etc.) and updated when funders release new requirements

- Offers templates to create a DMSP for NIH grants

- Uses step-by-step wizard to create a plan.

- Possible to co-create/edit plans

- Plans can be transported to ORCID records.


Sample NIH DMS Plans

DMS Plan Template in Word


Useful Links / Resources

ØNIH Data Sharing Policy website -

ØFinal NIH Policy for Data Management and Sharing -

ØSupplemental Information to the NIH Policy for Data Management and Sharing: Elements of an NIH Data Management and Sharing Plan -

ØSupplemental Information to the NIH Policy for Data Management and Sharing: Allowable Costs for Data Management and Sharing -

Ø Supplemental Information to the NIH Policy for Data Management and Sharing: Selecting a Repository for Data Resulting from NIH-Supported Research -

ØNIH Scientific Data Sharing - (Planning and Budgeting for Data Management & Sharing) | Data Management | Sharing Scientific Data)

ØPlanning and Budgeting for Data Management & Sharing - (Write a DMSP | Budgeting for DMS)

ØData Management -

ØSharing Scientific Data -


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